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Co-Designing with Algorithms: Unpacking the Complex Role of GenAI in Interactive System Design Education

Hauke Sandhaus, Quiquan Gu, Maria Teresa Parreira, Wendy Ju

TL;DR

This study investigates how GenAI tools shape graduate students' interactive device design workflows in an HCI course. Through post-course interviews with 17 students across 13 groups, the authors identify nine primary GenAI use cases and a four-way usage taxonomy (Benchmark, Booster, Executer, Amplifier), revealing that outcomes depend more on usage approach than task type. Benefits include faster iterations and expanded ideation, while risks involve shallow learning and reduced reflective practice, prompting curriculum and policy recommendations. The findings underscore the need to integrate GenAI thoughtfully into HCI education—promoting critical thinking, transparent attribution, and equity—so future designers can harness GenAI for creative co-design without compromising core design learning. The work informs educators about adapting learning objectives, assessment methods, and tool customization to balance efficiency with deep learning and ethical practice.

Abstract

Generative Artificial Intelligence (GenAI) is transforming Human-Computer Interaction (HCI) education and technology design, yet its impact remains poorly understood. This study explores how graduate students in an applied HCI course used GenAI tools during interactive device design. Despite no encouragement, all groups integrated GenAI into their workflows. Through 12 post-class group interviews, we identified how GenAI co-design behaviors present both benefits, such as enhanced creativity and faster design iterations, and risks, including shallow learning and reflection. Benefits were most evident during the execution phases, while the discovery and reflection phases showed limited gains. A taxonomy of usage patterns revealed that students' outcomes depended more on how they used GenAI than the specific tasks performed. These findings highlight the need for HCI education to adapt to GenAI's role and offer recommendations for curricula to better prepare future designers for effective creative co-design.

Co-Designing with Algorithms: Unpacking the Complex Role of GenAI in Interactive System Design Education

TL;DR

This study investigates how GenAI tools shape graduate students' interactive device design workflows in an HCI course. Through post-course interviews with 17 students across 13 groups, the authors identify nine primary GenAI use cases and a four-way usage taxonomy (Benchmark, Booster, Executer, Amplifier), revealing that outcomes depend more on usage approach than task type. Benefits include faster iterations and expanded ideation, while risks involve shallow learning and reduced reflective practice, prompting curriculum and policy recommendations. The findings underscore the need to integrate GenAI thoughtfully into HCI education—promoting critical thinking, transparent attribution, and equity—so future designers can harness GenAI for creative co-design without compromising core design learning. The work informs educators about adapting learning objectives, assessment methods, and tool customization to balance efficiency with deep learning and ethical practice.

Abstract

Generative Artificial Intelligence (GenAI) is transforming Human-Computer Interaction (HCI) education and technology design, yet its impact remains poorly understood. This study explores how graduate students in an applied HCI course used GenAI tools during interactive device design. Despite no encouragement, all groups integrated GenAI into their workflows. Through 12 post-class group interviews, we identified how GenAI co-design behaviors present both benefits, such as enhanced creativity and faster design iterations, and risks, including shallow learning and reflection. Benefits were most evident during the execution phases, while the discovery and reflection phases showed limited gains. A taxonomy of usage patterns revealed that students' outcomes depended more on how they used GenAI than the specific tasks performed. These findings highlight the need for HCI education to adapt to GenAI's role and offer recommendations for curricula to better prepare future designers for effective creative co-design.

Paper Structure

This paper contains 43 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. GenAI’s Promise for Technology Design and HCI Practice
  4. GenAI’s Risks for Technology Design and Education
  5. Positive Impact of GenAI on Novice HCI Practice
  6. Unexplored Challenges: GenAI's Role in HCI Education
  7. Method
  8. HCI Class on Design of Interactive Devices
  9. Class Policy
  10. Participants
  11. Interview Process
  12. Interview Analysis
  13. Positionality and Authorship
  14. Results
  15. What Students' Use GenAI for in Interactive System Design
  16. ...and 28 more sections

Figures (7)

  • Figure 1: Course Delivery Timeline
  • Figure 2: Students take diverse strategies when interacting with GenAI for storyboarding.
  • Figure 3: 3D model of Bender from Futurama, with hardware integration instructions generated by ChatGPT (I1).
  • Figure 4: Student's sentiment about each GenAI use case. Data in proportion to reported sentiments.
  • Figure 5: An interaction illustrating a student's struggle with repeated GenAI prompting. The student attempts to resolve a bug by repeatedly submitting the complete code and associated error messages to a GenAI tool, ultimately failing to address what is revealed to be a minor issue (I9). More GenAI interactions archived on OSF Sandhaus_Gu_Parreira_Ju_2024.
  • ...and 2 more figures